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Installation instructions for Ubuntu 16.04

  • Make sure CUDA and cuDNN are installed. Three configurations have been tested:

    • TensorFlow 1.4.1, CUDA 8.0 and cuDNN 6.0
    • TensorFlow 1.12.0, CUDA 9.0 and cuDNN 7.4
    • TensorFlow 1.13.0, CUDA 10.0 and cuDNN 7.5 (bug found only with this version).

  • Ensure all python packages are installed :

        sudo apt update
    sudo apt install python3-dev python3-pip python3-tk

  • Follow Tensorflow installation procedure.

  • Install the other dependencies with pip:

    • numpy
    • scikit-learn
    • psutil
    • matplotlib (for visualization)
    • mayavi (for visualization)
    • PyQt5 (for visualization)

  • Compile the customized Tensorflow operators located in tf_custom_ops. Open a terminal in this folder, and run:

        sh compile_op.sh

    N.B. If you installed Tensorflow in a virtual environment, it needs to be activated when running these scripts

  • Compile the C++ extension module for python located in cpp_wrappers. Open a terminal in this folder, and run:

        sh compile_wrappers.sh

You should now be able to train Kernel-Point Convolution models

Installation instructions for Ubuntu 18.04 (Thank to @noahtren)

  • Remove the -D_GLIBCXX_USE_CXX11_ABI=0 flag for each line in tf_custom_ops/compile_op.sh (problem with the version of gcc). One configuration has been tested:

    • TensorFlow 1.12.0, CUDA 9.0 and cuDNN 7.3.1